Feature extraction of turbine blade based on GWO-VMD
Gas turbine blades were an important part of gas turbines.They worked in the harsh environment of high temperature,high pressure,and high speed for a long time,and healthy turbine blades could ensure the high efficiency of gas turbines.Temperature was a key parameter to reflect the state of the turbine blades of a gas turbine.When the turbine blades suffered from failures such as coating peeling and fracture,the surface temperature of the blades changed.In order to measure the health status of the turbine blades,blade temperature,an important indicator of blade quality was used.The data were preprocessed and feature extraction was performed,and the turbine blade features were clustered.Based on GWO-VMD,the four entropy features of each IMF component of the decomposed leaf temperature signal were extracted,and PCA was used to reduce the dimensionality of the extracted leaf features.The dimensionality-reduced features were sent to the FCM clustering algorithm,and the optimal number of clusters was obtained by the silhouette coefficient and SSE.The clustering of leaf features was realized.The clustering effect showed that the feature extraction method could better distinguish the health status of different turbine blades,which provided a certain idea for the research of turbine blade health monitoring.
turbine bladefeature extractiongray wolf optimization algorithmvariational mode decompositionfuzzy C-means clustering
万方数据
维普
